An Operational Semantics for Knowledge Bases

Ronald Fagin, Joseph Y. Halpern, Yoram Moses, Moshe Y. Vardi

The standard approach in AI to knowledge representation is to represent an agent’s knowledge symbolically as a collection of formulas, which we can view as a knowledge base. An agent is then said to know a fact if it is provable from the formulas in his knowledge base. Halpern and Vardi advocated a model-theoretic approach to knowledge representation. In this approach, the key step is representing the agent’s knowledge using an appropriate semantic model. Here, we model knowledge bases operationally as multi-agent systems. Our results show that this approach offers significant advantages.


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